A Structural Equation Modeling of Factors Affecting Student Motivation in Thesis Preparation

Abstract

Structural equation modelling is a multivariate statistical analysis technique that is used to analyse structural relationships. This technique is the combination of factor analysis and multiple regression analysis, and it is used to analyse the structural relationship between measured variables and latent constructs. The purpose of this study is to use structural equation modelling to better understand student motivation in thesis preparation and its causal determinants. The study creates a plausible structural equation model (SEM) and tests it. The data used were students’ responses of a questionnaire survey about the student motivation in thesis preparation. Based on the results of the study, it was found that the relationship between lecturer and student and the environmental conditions have significant influence to student motivation in thesis preparation.

Evaluating an Art and Design Students’ Gender-Based Entrepreneurial Potential Model

“…The SEM is a multivariate statistical analysis technique combining aspects of factor analysis and multiple regression analysis to explore and assess the structural relationships between observed variables and latent constructs in a given dataset (Setiawan et al, 2020). The SEM holds broad applicability due to its quantitative approach in testing substantive theories and explicitly considers the presence of measurement error, a pervasive factor in most disciplines (Civelek, 2018).…”

Analysis of Food Security Factors in Indonesia using SEM-GSCA with the Alternating Least Squares Method

“…To comprehensively understand the factors influencing food security in Indonesia, an adequate analytical method is required. One effective method is the use of Structural Equation Modeling (SEM), which is a conceptual modeling approach widely used to answer any research question related to the indirect (latent) or direct observation of one or more dependent and/or independent variables (Sarstedt et al, 2022;Setiawan et al, 2021). In line with this, in this research SEM was chosen as the analytical method because of its ability to analyse complex relationships between latent variables and observed variables, assessing various factors that influence food security simultaneously.…”